Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) technologies are rapidly being deployed to develop AI-powered applications, including AI fitness apps. These AI-enhanced fitness apps are more intelligent and human-like than their conventional counterparts, offering users a highly engaging and personalized experience. However, the underlying mechanisms driving these experiences require further exploration. This research focuses on two key attributes of AI—perceived intelligence (PI) and perceived anthropomorphism (PA)—and investigates their effects on user behavioral intentions through various gratifications in the context of AI fitness apps. Using survey data from 408 participants, our empirical findings reveal that PI and PA significantly influence users' intentions to use and purchase AI fitness apps, primarily by enhancing gratifications such as exercise enjoyment, social presence, and social interaction. These findings provide valuable insights and guidance for fitness app developers on leveraging AI technologies to optimize app functionalities and enhance user engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it